Cherry-pick: Fix torchrl scripts for PT 2.6 TorchRL>=0.6 (#3199) (#3266)

Fixes #3195
Fixing TorchRL scripts for Pytorch 2.6 release

Co-authored-by: Vincent Moens <vmoens@meta.com>

Author: svekars

.jenkins/validate_tutorials_built.py

@@ -51,7 +51,6 @@
     "intermediate_source/text_to_speech_with_torchaudio",
     "intermediate_source/tensorboard_profiler_tutorial", # reenable after 2.0 release.
     "intermediate_source/torch_export_tutorial", # reenable after 2940 is fixed.
-    "advanced_source/pendulum",
 ]
 
 def tutorial_source_dirs() -> List[Path]:

advanced_source/coding_ddpg.py

@@ -893,7 +893,7 @@ def make_recorder(actor_model_explore, transform_state_dict, record_interval):
         record_frames=1000,
         policy_exploration=actor_model_explore,
         environment=environment,
-        exploration_type=ExplorationType.MEAN,
+        exploration_type=ExplorationType.DETERMINISTIC,
         record_interval=record_interval,
     )
     return recorder_obj

advanced_source/pendulum.py

@@ -604,7 +604,7 @@ def __init__(self, td_params=None, seed=None, device="cpu"):
     env,
     # ``Unsqueeze`` the observations that we will concatenate
     UnsqueezeTransform(
-        unsqueeze_dim=-1,
+        dim=-1,
         in_keys=["th", "thdot"],
         in_keys_inv=["th", "thdot"],
     ),

intermediate_source/dqn_with_rnn_tutorial.py

@@ -433,7 +433,7 @@
     exploration_module.step(data.numel())
     updater.step()
 
-    with set_exploration_type(ExplorationType.MODE), torch.no_grad():
+    with set_exploration_type(ExplorationType.DETERMINISTIC), torch.no_grad():
         rollout = env.rollout(10000, stoch_policy)
         traj_lens.append(rollout.get(("next", "step_count")).max().item())
 

intermediate_source/reinforcement_ppo.py

@@ -419,8 +419,8 @@
     in_keys=["loc", "scale"],
     distribution_class=TanhNormal,
     distribution_kwargs={
-        "min": env.action_spec.space.low,
-        "max": env.action_spec.space.high,
+        "low": env.action_spec.space.low,
+        "high": env.action_spec.space.high,
     },
     return_log_prob=True,
     # we'll need the log-prob for the numerator of the importance weights